The precise characterization of hypersonic glide vehicle(HGV) maneuver laws in complex flight scenarios still faces challenges. Non-stationary changes in flight state due to abrupt changes in maneuver modes place high...The precise characterization of hypersonic glide vehicle(HGV) maneuver laws in complex flight scenarios still faces challenges. Non-stationary changes in flight state due to abrupt changes in maneuver modes place high demands on the accuracy of modeling methods. To address this issue, a novel maneuver laws modeling and analysis method based on higher order multi-resolution dynamic mode decomposition(HMDMD) is proposed in this work. A joint time-space-frequency decomposition of the vehicle's state sequence in the complex flight scenario is achieved with the higher order Koopman assumption and standard multi-resolution dynamic mode decomposition, and an approximate dynamic model is established. The maneuver laws can be reconstructed and analyzed with extracted multi-scale spatiotemporal modes with clear physical meaning. Based on the dynamic model of HGV, two flight scenarios are established with constant angle of attack and complex maneuver laws, respectively. Simulation results demonstrate that the maneuver laws obtained using the HMDMD method are highly consistent with those derived from the real dynamic model, the modeling accuracy is better than other common modeling methods, and the method has strong interpretability.展开更多
This paper presents a block-based secure and robust watermarking technique for color images based on multi-resolution decomposition and de-correlation.The principal objective of the presented scheme is to simultaneous...This paper presents a block-based secure and robust watermarking technique for color images based on multi-resolution decomposition and de-correlation.The principal objective of the presented scheme is to simultaneously meet all the four requirements(robustness,security,imperceptibility,and capacity)of a good watermarking scheme.The contribution of this study is to basically achieve the four contradictory requirements that a good watermarking scheme must meet.To do so,different approaches are combined in a way that the four requirements are achieved.For instance,to obtain imperceptibility,the three color channels(red,green,and blue)are de-correlated using principal component analysis,and the first principal component(de-correlated red channel)is chosen for watermark embedding.Afterwards,to achieve robustness,the de-correlated channel is decomposed using a discrete wavelet transform(DWT),and the approximate band(the other three bands are kept intact to preserve the edge information)is further decomposed into distinct blocks.The random blocks are chosen based on a random generated key.The random selected blocks are further broken down into singular values and vectors.Based on the mutual dependency on singular values and vectors’matrices,the values are modified depending on the watermarking bits,and their locations are saved and used as another key,required when the watermark is to be extracted.Consequently,two-level authentication levels ensure the security,and using both singular values and vectors increases the capacity of the presented scheme.Moreover,the involvement of both left and right singular vectors along with singular values in the watermarking embedding process strengthens the robustness of the proposed scheme.Finally,to compare the presented scheme with the state-of-the-art schemes in terms of imperceptibility(peak signal-to-noise ratio and structural similarity index),security(with numerous fake keys),robustness(normalized correlation and bit error rate),and capacity,the Gonzalez and Kodak datasets are used.The comparison shows significant improvement of the proposed scheme over existing schemes.展开更多
Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition wi...Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to investigate ozone dynamics and formation regimes in a coastal area of China.During the period of 2017–2022,significant inter-annual fluctuations emerged,with peaks in mid-2017 attributed to volatile organic compounds(VOCs),and in late-2019 influenced by air temperature.Multifaceted periodicities(daily,weekly,holiday,and yearly)in ozone were revealed,elucidating substantial influences of daily and yearly components on ozone periodicity.A VOC-sensitive ozone formation regime was identified,characterized by lower VOCs/NO_(x) ratios(average=0.88)and significant positive correlations between ozone and VOCs.This interplay manifested in elevated ozone duringweekends,holidays,and pandemic lockdowns.Key variables influencing ozone across diverse timescaleswere uncovered,with solar radiation and temperature driving daily and yearly ozone variations,respectively.Precursor substances,particularly VOCs,significantly shaped weekly/holiday patterns and long-term trends of ozone.Specifically,acetone,ethane,hexanal,and toluene had a notable impact on the multi-year ozone trend,emphasizing the urgency of VOC regulation.Furthermore,our observations indicated that NO_(x) primarily drived the stochastic variations in ozone,a distinguishing characteristic of regions with heavy traffic.This research provides novel insights into ozone dynamics in coastal urban areas and highlights the importance of integrating statistical and machinelearning methods in atmospheric pollution studies,with implications for targeted mitigation strategies beyond this specific region and pollutant.展开更多
Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the origin...Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.展开更多
Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. Ho...Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. However, such fascinating properties do not bring long-term cyclability of h-LMBs. One of critical challenges is the interface instability in contacting with the Li metal anode, as fiuorinated solvents are highly susceptible to exceptionally reductive metallic Li attributed to its low lowest unoccupied molecular orbital(LUMO), which leads to significant consumption of the fiuorinated components upon cycling.Herein, attenuating reductive decomposition of fiuorinated electrolytes is proposed to circumvent rapid electrolyte consumption. Specifically, the vinylene carbonate(VC) is selected to tame the reduction decomposition by preferentially forming protective layer on the Li anode. This work, experimentally and computationally, demonstrates the importance of pre-passivation of Li metal anodes at high voltage to attenuate the decomposition of fiuoroethylene carbonate(FEC). It is expected to enrich the understanding of how VC attenuate the reactivity of FEC, thereby extending the cycle life of fiuorinated electrolytes in high-voltage Li-metal batteries.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
In this paper,we prove that L(K_(x,y))(λ),theλ-fold line graph of the complete bipartite graph Ka,y,has a C_(6)-decomposition if and only if ry≥6,λxy(c+y-2)=0(mod 12)and(x+y)=0(mod 2),where x,y are nonnegative int...In this paper,we prove that L(K_(x,y))(λ),theλ-fold line graph of the complete bipartite graph Ka,y,has a C_(6)-decomposition if and only if ry≥6,λxy(c+y-2)=0(mod 12)and(x+y)=0(mod 2),where x,y are nonnegative integers and(x,y)≠(2,4)or(2,5).展开更多
To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explo...To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.展开更多
In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to ...In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to evaluate the effectiveness of exteriorwall coat-ings with ozone decomposition catalysts for ozone removal in practical applications.ANSYS 2020R1 software was first used for simulation and analysis of ozone concentration and flow fields to investigate the decomposition boundary of these wall coatings.The results show that the exterior wall coatings with manganese-based catalysts can effectively reduce the ozone concentration near the wall coating.The ozone decomposition efficiency is nega-tively correlated with the distance fromthe coating and the decomposition boundary range is around 18 m.The decomposition boundary will increase with the increase of tempera-ture,and decrease with the increase of the wind speed and the relative humidity.These results underscore the viability of using exterior wall coatings with catalysts for controlling ozone pollution in atmospheric environments.This approach presents a promising avenue for addressing ozone pollution through self-purifying materials on building external wall.展开更多
In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrabilit...In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.展开更多
A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data a...A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data and applying multi-resolution analysis in cumulant reconstruction.A motor-driven rotating mask optical modulation system is designed to adjust the excitation lightfield and allows for fast deployment.Active-modulated fluorescence fluctuation superresolution microscopy with multi-resolution analysis(AMF-MRA-SOFI)demonstrates enhanced resolution ability and reconstruction quality in experiments performed on sample of conventional dyes,achieving a resolution of 100 nm in the fourth order compared to conventional SOFI reconstruction.Furthermore,our approach combining expansion super-resolution achieved a resolution at-57 nm.展开更多
We presented the preparation and analysis of La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts,supported on microwave-absorbing ceramic carriers,using the sol-gel method.We systematically investigated the effects of various re...We presented the preparation and analysis of La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts,supported on microwave-absorbing ceramic carriers,using the sol-gel method.We systematically investigated the effects of various reaction conditions under microwave irradiation(0-50 W).These conditions included reaction temperatures(300-600℃),oxygen concentrations(0-6%),and varying K^(+)doping levels on the catalysts'activity.The crystalline phase,microstructure,and the catalytic activity of the catalyst were analyzed by XRD,TEM,H_2-TPR,and O_(2)-TPD.The experimental results reveal that La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts consistently form homogeneous perovskite nanoparticles across different doping levels.The NO decomposition efficiency on these catalysts initially increases and then decreases with variations in doping amount,temperature,and microwave power.Additionally,an increase in oxygen concentration positively influences NO conversion rates.The optimal performance is observed with La_(0.7)K_(0.3)CoO_(3)catalyst under conditions of x=0.3,400℃,10 W microwave power,and 4%oxygen concentration,achieving a peak NO conversion rate of La_(0.7)K_(0.3)CoO_(3)catalyst is 93.1%.展开更多
As an independent thermodynamic parameter,pressure significantly influences interatomic distances,leading to an increase in material density.In this work,we employ the CALYPSO structure search and density functional t...As an independent thermodynamic parameter,pressure significantly influences interatomic distances,leading to an increase in material density.In this work,we employ the CALYPSO structure search and density functional theory calculations to explore the structural phase transitions and electronic properties of calcium-sulfur compounds(Ca_(x)S_(1-x),where x=1/4,1/3,1/2,2/3,3/4,4/5)under 0-1200 GPa.The calculated formation enthalpies suggest that Ca_(x)S_(1-x)compounds undergo multiple phase transitions and eventually decompose into elemental Ca and S,challenging the traditional view that pressure stabilizes and densifies compounds.The analysis of formation enthalpy indicates that an increase in pressure leads to a rise in internal energy and the PV term,resulting in thermodynamic instability.Bader charge analysis reveals that this phenomenon is attributed to a decrease in charge transfer under high pressure.The activation of Ca-3d orbitals is significantly enhanced under pressure,leading to competition with Ca-4s orbitals and S-3p orbitals.This may cause the formation enthalpy minimum on the convex hull to shift sequentially from CaS to CaS_(3),then to Ca_(3)S and Ca_(2)S,and finally back to CaS.These findings provide critical insights into the behavior of alkaline-earth metal sulfides under high pressure,with implications for the synthesis and application of novel materials under extreme conditions and for understanding element distribution in planetary interiors.展开更多
Organic matter(OM)derived from the decomposition of crop residues plays a key role as a sorbent for cadmium(Cd)immobilization.Few studies have explored the straw decomposition processes with the presence of minerals,a...Organic matter(OM)derived from the decomposition of crop residues plays a key role as a sorbent for cadmium(Cd)immobilization.Few studies have explored the straw decomposition processes with the presence of minerals,and the effect of newly generated organomineral complexes on heavy metal adsorption.In this study,we investigated the variations in structure and composition during the rice straw decomposition with or without minerals(goethite and kaolinite),as well as the adsorption behavior and mechanisms by which straw decomposition affects Cd immobilization.The degree of humification of extracted straw organic matter was assessed using excitation-emission matrix(EEM)fluorescence and Ultraviolet-visible spectroscopy(UV-vis),while employing FTIR spectroscopy and XPS to characterize the adsorption mechanisms.The spectra analysis revealed the enrichment of highly aromatic and hydrophobic components,indicating that the degree of straw decomposition and humification were further intensified during incubation.Additionally,the existence of goethite(SG)accelerated the humification of OM.Sorption experiments revealed that the straw humification increased Cd adsorption capacity.Notably,SG exhibited significantly higher adsorption performance compared to the organic matter without minerals(RS)and the existence of kaolinite(SK).Further analysis using FT-IR spectroscopy and XPS verified that the primarymechanisms involved in Cd immobilization were complexion with—OH and—COOH,as well as the formation of Cd-πbinds with aromatic C=C on the surface of solid OMs.These findings will facilitate understanding the interactions of the rice straw decomposing with soil minerals and its remediation effect on Cd-contaminated farmland.展开更多
Formalizing complex processes and phenomena of a real-world problem may require a large number of variables and constraints,resulting in what is termed a large-scale optimization problem.Nowadays,such large-scale opti...Formalizing complex processes and phenomena of a real-world problem may require a large number of variables and constraints,resulting in what is termed a large-scale optimization problem.Nowadays,such large-scale optimization problems are solved using computing machines,leading to an enormous computational time being required,which may delay deriving timely solutions.Decomposition methods,which partition a large-scale optimization problem into lower-dimensional subproblems,represent a key approach to addressing time-efficiency issues.There has been significant progress in both applied mathematics and emerging artificial intelligence approaches on this front.This work aims at providing an overview of the decomposition methods from both the mathematics and computer science points of view.We also remark on the state-of-the-art developments and recent applications of the decomposition methods,and discuss the future research and development perspectives.展开更多
Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special...Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special kind of directed acyclic graph with same node and edge types as the heterogeneous graph.However,how to design proper metagraphs is challenging.Recently,there have been many works on learning suitable metagraphs from a heterogeneous graph.Existing methods generally introduce continuous weights for edges that are independent of each other,which ignores the topological structures of meta-graphs and can be ineffective.To address this issue,the authors propose a new viewpoint from tensor on learning meta-graphs.Such a viewpoint not only helps interpret the limitation of existing works by CANDECOMP/PARAFAC(CP)decomposition,but also inspires us to propose a topology-aware tensor decomposition,called TENSUS,that reflects the structure of DAGs.The proposed topology-aware tensor decomposition is easy to use and simple to implement,and it can be taken as a plug-in part to upgrade many existing works,including node classification and recommendation on heterogeneous graphs.Experimental results on different tasks demonstrate that the proposed method can significantly improve the state-of-the-arts for all these tasks.展开更多
When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and ne...When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers.Using a tensor decomposition method,we identify trans-acting expression quantitative trait loci(trans-eQTLs)linked to gene modules,rather than individual genes,which were associated with maize drought response.Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits.Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules,the majority of which cannot be detected based on individual gene expression.Notably,the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection.We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans,as exemplified by two transcription factor genes.Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding.展开更多
The deployment of non-precious metal catalysts for the production of COx-free hydrogen via the ammonia decomposition reaction(ADR)presents a promising yet great challenge.In the present study,two crystal structures of...The deployment of non-precious metal catalysts for the production of COx-free hydrogen via the ammonia decomposition reaction(ADR)presents a promising yet great challenge.In the present study,two crystal structures of α-MoC and β-Mo_(2)C catalysts with different Mo/C ratios were synthesized,and their ammonia decomposition performance as well as structural evolution in ADR was investigated.The β-Mo_(2)C catalyst,characterized by a higher Mo/C ratio,demonstrated a remarkable turnover frequency of 1.3 s^(-1),which is over tenfold higher than that ofα-MoC(0.1 s^(-1)).An increase in the Mo/C ratio of molybdenum carbide revealed a direct correlation between the surface Mo/C ratio and the hydrogen yield.The transient response surface reaction indicated that the combination of N*and N*derived from NH_(3) dissociation represents the rate-determining step in the ADR,andβ-Mo2C exhibited exceptional proficiency in facilitating this pivotal step.Concurrently,the accumulation of N*species on the carbide surface could induce the phase transition of molybdenum carbide to nitride,which follows a topological transformation.It is discovered that such phase evolution was affected by the Mo-C surface and reaction temperature simultaneously.When the kinetics of combination of N*was accelerated by rising temperatures and its accumulation on the carbide surface was mitigated,β-Mo_(2)C maintained its carbide phase,preventing nitridation during the ADR at 810℃.Our results contribute to an in-depth understanding of the molybdenum carbides’catalytic properties in ADR and highlight the nature of the carbide-nitride phase transition in the reaction.展开更多
High entropy alloys(HEAs)constituted of single solid solution phase,but remains chemical inhomogeneity in nature due to its multi-principal composition.Currently,existence of nanoscale spinodal decomposition(SD)phase ...High entropy alloys(HEAs)constituted of single solid solution phase,but remains chemical inhomogeneity in nature due to its multi-principal composition.Currently,existence of nanoscale spinodal decomposition(SD)phase in matrix was found to have significant impact on the properties of HEAs.Nevertheless,the morphology evolution and the kinetics of SD is not clear,which hinders in-depth understanding of the structure-property relationship.In this study,we examine the spinodal structures in(FeCoCrNi)85(AlCu)15 HEAs at different states using in-situ small-angle neutron scattering(SANS),in conjunction with transmission electron microscopy technique.The result demonstrates that SD occurred when aging the HEA samples at temperatures ranging from 500 to 800℃,which leads to the phase constitution of NiAlCu-rich and FeCoCr-rich spinodal phases,L1_(2)ordered phases,and FCC matrix.The characteristic wavelength of SD(λ_(SD))grows from 5.31 to 51.26 nm when aging temperature rises from 500 to 800℃,which explains the enhancement of the alloy’s microhardness.The SD kinetics was unraveled by fitting the time-dependentλ_(SD)through in-situ SANS measurement at 700℃.During isothermal treatment at 700℃,theλ_(SD)increases from 10.42 to 17.43 nm with prolonged time,and SD is in the late stage from the exponential trend of theλ_(SD)over time.Moreover,comparing with aging temperature,the aging time has a relatively minor impact on the coarsening of SD.展开更多
Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving fac...Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 12302056)the Postdoctoral Fellowship Program of CPSF:GZC20233445。
文摘The precise characterization of hypersonic glide vehicle(HGV) maneuver laws in complex flight scenarios still faces challenges. Non-stationary changes in flight state due to abrupt changes in maneuver modes place high demands on the accuracy of modeling methods. To address this issue, a novel maneuver laws modeling and analysis method based on higher order multi-resolution dynamic mode decomposition(HMDMD) is proposed in this work. A joint time-space-frequency decomposition of the vehicle's state sequence in the complex flight scenario is achieved with the higher order Koopman assumption and standard multi-resolution dynamic mode decomposition, and an approximate dynamic model is established. The maneuver laws can be reconstructed and analyzed with extracted multi-scale spatiotemporal modes with clear physical meaning. Based on the dynamic model of HGV, two flight scenarios are established with constant angle of attack and complex maneuver laws, respectively. Simulation results demonstrate that the maneuver laws obtained using the HMDMD method are highly consistent with those derived from the real dynamic model, the modeling accuracy is better than other common modeling methods, and the method has strong interpretability.
文摘This paper presents a block-based secure and robust watermarking technique for color images based on multi-resolution decomposition and de-correlation.The principal objective of the presented scheme is to simultaneously meet all the four requirements(robustness,security,imperceptibility,and capacity)of a good watermarking scheme.The contribution of this study is to basically achieve the four contradictory requirements that a good watermarking scheme must meet.To do so,different approaches are combined in a way that the four requirements are achieved.For instance,to obtain imperceptibility,the three color channels(red,green,and blue)are de-correlated using principal component analysis,and the first principal component(de-correlated red channel)is chosen for watermark embedding.Afterwards,to achieve robustness,the de-correlated channel is decomposed using a discrete wavelet transform(DWT),and the approximate band(the other three bands are kept intact to preserve the edge information)is further decomposed into distinct blocks.The random blocks are chosen based on a random generated key.The random selected blocks are further broken down into singular values and vectors.Based on the mutual dependency on singular values and vectors’matrices,the values are modified depending on the watermarking bits,and their locations are saved and used as another key,required when the watermark is to be extracted.Consequently,two-level authentication levels ensure the security,and using both singular values and vectors increases the capacity of the presented scheme.Moreover,the involvement of both left and right singular vectors along with singular values in the watermarking embedding process strengthens the robustness of the proposed scheme.Finally,to compare the presented scheme with the state-of-the-art schemes in terms of imperceptibility(peak signal-to-noise ratio and structural similarity index),security(with numerous fake keys),robustness(normalized correlation and bit error rate),and capacity,the Gonzalez and Kodak datasets are used.The comparison shows significant improvement of the proposed scheme over existing schemes.
基金supported by Ningbo Natural Science Foundation(No.2023J059)Ningbo Commonweal Programme Key Project(No.2023S038)Guangxi Key Research and Development Programme(No.GuikeAB21220063).
文摘Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to investigate ozone dynamics and formation regimes in a coastal area of China.During the period of 2017–2022,significant inter-annual fluctuations emerged,with peaks in mid-2017 attributed to volatile organic compounds(VOCs),and in late-2019 influenced by air temperature.Multifaceted periodicities(daily,weekly,holiday,and yearly)in ozone were revealed,elucidating substantial influences of daily and yearly components on ozone periodicity.A VOC-sensitive ozone formation regime was identified,characterized by lower VOCs/NO_(x) ratios(average=0.88)and significant positive correlations between ozone and VOCs.This interplay manifested in elevated ozone duringweekends,holidays,and pandemic lockdowns.Key variables influencing ozone across diverse timescaleswere uncovered,with solar radiation and temperature driving daily and yearly ozone variations,respectively.Precursor substances,particularly VOCs,significantly shaped weekly/holiday patterns and long-term trends of ozone.Specifically,acetone,ethane,hexanal,and toluene had a notable impact on the multi-year ozone trend,emphasizing the urgency of VOC regulation.Furthermore,our observations indicated that NO_(x) primarily drived the stochastic variations in ozone,a distinguishing characteristic of regions with heavy traffic.This research provides novel insights into ozone dynamics in coastal urban areas and highlights the importance of integrating statistical and machinelearning methods in atmospheric pollution studies,with implications for targeted mitigation strategies beyond this specific region and pollutant.
基金supported in part by the Interdisciplinary Project of Dalian University(DLUXK-2023-ZD-001).
文摘Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.
基金supported by the National Natural Science Foundation of China (Nos. 22379121, 62005216)Basic Public Welfare Research Program of Zhejiang (No. LQ22F050013)+1 种基金Zhejiang Province Key Laboratory of Flexible Electronics Open Fund (2023FE005)Shenzhen Foundation Research Program (No. JCYJ20220530112812028)。
文摘Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. However, such fascinating properties do not bring long-term cyclability of h-LMBs. One of critical challenges is the interface instability in contacting with the Li metal anode, as fiuorinated solvents are highly susceptible to exceptionally reductive metallic Li attributed to its low lowest unoccupied molecular orbital(LUMO), which leads to significant consumption of the fiuorinated components upon cycling.Herein, attenuating reductive decomposition of fiuorinated electrolytes is proposed to circumvent rapid electrolyte consumption. Specifically, the vinylene carbonate(VC) is selected to tame the reduction decomposition by preferentially forming protective layer on the Li anode. This work, experimentally and computationally, demonstrates the importance of pre-passivation of Li metal anodes at high voltage to attenuate the decomposition of fiuoroethylene carbonate(FEC). It is expected to enrich the understanding of how VC attenuate the reactivity of FEC, thereby extending the cycle life of fiuorinated electrolytes in high-voltage Li-metal batteries.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
文摘In this paper,we prove that L(K_(x,y))(λ),theλ-fold line graph of the complete bipartite graph Ka,y,has a C_(6)-decomposition if and only if ry≥6,λxy(c+y-2)=0(mod 12)and(x+y)=0(mod 2),where x,y are nonnegative integers and(x,y)≠(2,4)or(2,5).
基金Supported by the National Key R&D Program of China(2023YFD2101001)National Natural Science Foundation of China(32202144,61807001)。
文摘To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.
基金supported by the National Natural Science Foundation of China(Nos.52470114 and 52022104)the National Key R&D Program of China(No.2022YFC3702802)the Youth Innovation Promotion Association,CAS(No.Y2021020).
文摘In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to evaluate the effectiveness of exteriorwall coat-ings with ozone decomposition catalysts for ozone removal in practical applications.ANSYS 2020R1 software was first used for simulation and analysis of ozone concentration and flow fields to investigate the decomposition boundary of these wall coatings.The results show that the exterior wall coatings with manganese-based catalysts can effectively reduce the ozone concentration near the wall coating.The ozone decomposition efficiency is nega-tively correlated with the distance fromthe coating and the decomposition boundary range is around 18 m.The decomposition boundary will increase with the increase of tempera-ture,and decrease with the increase of the wind speed and the relative humidity.These results underscore the viability of using exterior wall coatings with catalysts for controlling ozone pollution in atmospheric environments.This approach presents a promising avenue for addressing ozone pollution through self-purifying materials on building external wall.
基金sponsored by the National Natural Science Foundations of China under Grant Nos.12301315,12235007,11975131the Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ20A010009。
文摘In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.
基金supported by the National Natural Science Foundation of China(62175034,62175036,32271510)the National Key R&D Program of China(2021YFF0502900)+2 种基金the Science and Technology Research Program of Shanghai(Grant No.19DZ2282100)the Shanghai Key Laboratory of Metasurfaces for Light Manipulation(23dz2260100)the Shanghai Engineering Technology Research Center of Hair Medicine(19DZ2250500).
文摘A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data and applying multi-resolution analysis in cumulant reconstruction.A motor-driven rotating mask optical modulation system is designed to adjust the excitation lightfield and allows for fast deployment.Active-modulated fluorescence fluctuation superresolution microscopy with multi-resolution analysis(AMF-MRA-SOFI)demonstrates enhanced resolution ability and reconstruction quality in experiments performed on sample of conventional dyes,achieving a resolution of 100 nm in the fourth order compared to conventional SOFI reconstruction.Furthermore,our approach combining expansion super-resolution achieved a resolution at-57 nm.
文摘We presented the preparation and analysis of La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts,supported on microwave-absorbing ceramic carriers,using the sol-gel method.We systematically investigated the effects of various reaction conditions under microwave irradiation(0-50 W).These conditions included reaction temperatures(300-600℃),oxygen concentrations(0-6%),and varying K^(+)doping levels on the catalysts'activity.The crystalline phase,microstructure,and the catalytic activity of the catalyst were analyzed by XRD,TEM,H_2-TPR,and O_(2)-TPD.The experimental results reveal that La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts consistently form homogeneous perovskite nanoparticles across different doping levels.The NO decomposition efficiency on these catalysts initially increases and then decreases with variations in doping amount,temperature,and microwave power.Additionally,an increase in oxygen concentration positively influences NO conversion rates.The optimal performance is observed with La_(0.7)K_(0.3)CoO_(3)catalyst under conditions of x=0.3,400℃,10 W microwave power,and 4%oxygen concentration,achieving a peak NO conversion rate of La_(0.7)K_(0.3)CoO_(3)catalyst is 93.1%.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11974154 and 12304278)the Taishan Scholars Special Funding for Construction Projects(Grant No.tstp20230622)+1 种基金the Natural Science Foundation of Shandong Province(Grant Nos.ZR2022MA004,ZR2023QA127,and ZR2024QA121)the Special Foundation of Yantai for Leading Talents above Provincial Level.
文摘As an independent thermodynamic parameter,pressure significantly influences interatomic distances,leading to an increase in material density.In this work,we employ the CALYPSO structure search and density functional theory calculations to explore the structural phase transitions and electronic properties of calcium-sulfur compounds(Ca_(x)S_(1-x),where x=1/4,1/3,1/2,2/3,3/4,4/5)under 0-1200 GPa.The calculated formation enthalpies suggest that Ca_(x)S_(1-x)compounds undergo multiple phase transitions and eventually decompose into elemental Ca and S,challenging the traditional view that pressure stabilizes and densifies compounds.The analysis of formation enthalpy indicates that an increase in pressure leads to a rise in internal energy and the PV term,resulting in thermodynamic instability.Bader charge analysis reveals that this phenomenon is attributed to a decrease in charge transfer under high pressure.The activation of Ca-3d orbitals is significantly enhanced under pressure,leading to competition with Ca-4s orbitals and S-3p orbitals.This may cause the formation enthalpy minimum on the convex hull to shift sequentially from CaS to CaS_(3),then to Ca_(3)S and Ca_(2)S,and finally back to CaS.These findings provide critical insights into the behavior of alkaline-earth metal sulfides under high pressure,with implications for the synthesis and application of novel materials under extreme conditions and for understanding element distribution in planetary interiors.
基金supported by the National Key Research and Development Program of China(No.2022YFD1700102)the National Natural Science Foundation of China(No.U20A20108)the Hunan Provincial Natural Science Foundation of China(No.2021JJ30357).
文摘Organic matter(OM)derived from the decomposition of crop residues plays a key role as a sorbent for cadmium(Cd)immobilization.Few studies have explored the straw decomposition processes with the presence of minerals,and the effect of newly generated organomineral complexes on heavy metal adsorption.In this study,we investigated the variations in structure and composition during the rice straw decomposition with or without minerals(goethite and kaolinite),as well as the adsorption behavior and mechanisms by which straw decomposition affects Cd immobilization.The degree of humification of extracted straw organic matter was assessed using excitation-emission matrix(EEM)fluorescence and Ultraviolet-visible spectroscopy(UV-vis),while employing FTIR spectroscopy and XPS to characterize the adsorption mechanisms.The spectra analysis revealed the enrichment of highly aromatic and hydrophobic components,indicating that the degree of straw decomposition and humification were further intensified during incubation.Additionally,the existence of goethite(SG)accelerated the humification of OM.Sorption experiments revealed that the straw humification increased Cd adsorption capacity.Notably,SG exhibited significantly higher adsorption performance compared to the organic matter without minerals(RS)and the existence of kaolinite(SK).Further analysis using FT-IR spectroscopy and XPS verified that the primarymechanisms involved in Cd immobilization were complexion with—OH and—COOH,as well as the formation of Cd-πbinds with aromatic C=C on the surface of solid OMs.These findings will facilitate understanding the interactions of the rice straw decomposing with soil minerals and its remediation effect on Cd-contaminated farmland.
基金The Australian Research Council(DP200101197,DP230101107).
文摘Formalizing complex processes and phenomena of a real-world problem may require a large number of variables and constraints,resulting in what is termed a large-scale optimization problem.Nowadays,such large-scale optimization problems are solved using computing machines,leading to an enormous computational time being required,which may delay deriving timely solutions.Decomposition methods,which partition a large-scale optimization problem into lower-dimensional subproblems,represent a key approach to addressing time-efficiency issues.There has been significant progress in both applied mathematics and emerging artificial intelligence approaches on this front.This work aims at providing an overview of the decomposition methods from both the mathematics and computer science points of view.We also remark on the state-of-the-art developments and recent applications of the decomposition methods,and discuss the future research and development perspectives.
基金National Key Research and Development Program of China,Grant/Award Number:2023YFB2903904。
文摘Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special kind of directed acyclic graph with same node and edge types as the heterogeneous graph.However,how to design proper metagraphs is challenging.Recently,there have been many works on learning suitable metagraphs from a heterogeneous graph.Existing methods generally introduce continuous weights for edges that are independent of each other,which ignores the topological structures of meta-graphs and can be ineffective.To address this issue,the authors propose a new viewpoint from tensor on learning meta-graphs.Such a viewpoint not only helps interpret the limitation of existing works by CANDECOMP/PARAFAC(CP)decomposition,but also inspires us to propose a topology-aware tensor decomposition,called TENSUS,that reflects the structure of DAGs.The proposed topology-aware tensor decomposition is easy to use and simple to implement,and it can be taken as a plug-in part to upgrade many existing works,including node classification and recommendation on heterogeneous graphs.Experimental results on different tasks demonstrate that the proposed method can significantly improve the state-of-the-arts for all these tasks.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04076)the Guangxi Key Research and Development Projects of China(GuikeAB21238004)the Agricultural Science and Technology Innovation Program.
文摘When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers.Using a tensor decomposition method,we identify trans-acting expression quantitative trait loci(trans-eQTLs)linked to gene modules,rather than individual genes,which were associated with maize drought response.Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits.Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules,the majority of which cannot be detected based on individual gene expression.Notably,the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection.We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans,as exemplified by two transcription factor genes.Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding.
文摘The deployment of non-precious metal catalysts for the production of COx-free hydrogen via the ammonia decomposition reaction(ADR)presents a promising yet great challenge.In the present study,two crystal structures of α-MoC and β-Mo_(2)C catalysts with different Mo/C ratios were synthesized,and their ammonia decomposition performance as well as structural evolution in ADR was investigated.The β-Mo_(2)C catalyst,characterized by a higher Mo/C ratio,demonstrated a remarkable turnover frequency of 1.3 s^(-1),which is over tenfold higher than that ofα-MoC(0.1 s^(-1)).An increase in the Mo/C ratio of molybdenum carbide revealed a direct correlation between the surface Mo/C ratio and the hydrogen yield.The transient response surface reaction indicated that the combination of N*and N*derived from NH_(3) dissociation represents the rate-determining step in the ADR,andβ-Mo2C exhibited exceptional proficiency in facilitating this pivotal step.Concurrently,the accumulation of N*species on the carbide surface could induce the phase transition of molybdenum carbide to nitride,which follows a topological transformation.It is discovered that such phase evolution was affected by the Mo-C surface and reaction temperature simultaneously.When the kinetics of combination of N*was accelerated by rising temperatures and its accumulation on the carbide surface was mitigated,β-Mo_(2)C maintained its carbide phase,preventing nitridation during the ADR at 810℃.Our results contribute to an in-depth understanding of the molybdenum carbides’catalytic properties in ADR and highlight the nature of the carbide-nitride phase transition in the reaction.
基金financially supported by the National Key Research and Development Program of China(Grant No.2021YFA1600701)the Guangdong Basic and Applied Basic Research Foundation,China(Project No.2021B1515140028)the National Natural Science Foundation of China(Grant No.12275154)。
文摘High entropy alloys(HEAs)constituted of single solid solution phase,but remains chemical inhomogeneity in nature due to its multi-principal composition.Currently,existence of nanoscale spinodal decomposition(SD)phase in matrix was found to have significant impact on the properties of HEAs.Nevertheless,the morphology evolution and the kinetics of SD is not clear,which hinders in-depth understanding of the structure-property relationship.In this study,we examine the spinodal structures in(FeCoCrNi)85(AlCu)15 HEAs at different states using in-situ small-angle neutron scattering(SANS),in conjunction with transmission electron microscopy technique.The result demonstrates that SD occurred when aging the HEA samples at temperatures ranging from 500 to 800℃,which leads to the phase constitution of NiAlCu-rich and FeCoCr-rich spinodal phases,L1_(2)ordered phases,and FCC matrix.The characteristic wavelength of SD(λ_(SD))grows from 5.31 to 51.26 nm when aging temperature rises from 500 to 800℃,which explains the enhancement of the alloy’s microhardness.The SD kinetics was unraveled by fitting the time-dependentλ_(SD)through in-situ SANS measurement at 700℃.During isothermal treatment at 700℃,theλ_(SD)increases from 10.42 to 17.43 nm with prolonged time,and SD is in the late stage from the exponential trend of theλ_(SD)over time.Moreover,comparing with aging temperature,the aging time has a relatively minor impact on the coarsening of SD.
基金the National Natural Science Foundation of China[Grant No.52270183].
文摘Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.
